CN109141675A - The method for being used for temperature-measuring system of distributed fibers noise reduction based on two points of SVD - Google Patents
The method for being used for temperature-measuring system of distributed fibers noise reduction based on two points of SVD Download PDFInfo
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- G01K—MEASURING TEMPERATURE; MEASURING QUANTITY OF HEAT; THERMALLY-SENSITIVE ELEMENTS NOT OTHERWISE PROVIDED FOR
- G01K11/00—Measuring temperature based upon physical or chemical changes not covered by groups G01K3/00, G01K5/00, G01K7/00 or G01K9/00
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Abstract
The invention discloses a kind of methods for being used for temperature-measuring system of distributed fibers noise reduction based on two points of SVD, first obtain two groups of initial data and are configured to two matrixes, two groups of initial data are anti-Stokes and Stokes initial data；Two matrixes are subjected to singular value decomposition respectively and are reconstructed, the matrix after forming two noise reductions；It obtains the data of noise reduction respectively from the matrix after noise reduction, and merges two groups of data and calculate temperature measurement result, maximum deviation, root-mean-square error and smoothness, calculated result quality coefficient；Using two points of singular value decompositions of successive ignition, the outcome quality coefficient of de-noising signal demodulation result after each iteration is obtained；Best the number of iterations is found, the corresponding noise reduction data of best the number of iterations are made into optimum signal.Noise-reduction method provided by the invention effectively improves the signal-to-noise ratio of acquired original signal, and reduces the maximum deviation and root-mean-square error of temperature measurement, and improve the smoothness of temperature curve.
Description
Technical field
The present invention relates to a kind of temp measuring system noise-reduction methods, more particularly to one kind to be used for distribution type fiber-optic based on two points of SVD
The method of temp measuring system noise reduction.
Background technique
With the fast development of optical fiber sensing technology, because of its electromagnetism interference and the distributed characteristic continuously measured, distribution
Formula optical fiber temperature measurement system has been widely used for some specific, under rugged environment peaces such as power grid, mine and nuclear environment
Full monitoring.However, the random noise of system increases measurement error, to be that temperature-measuring system of distributed fibers always exists urgently to be resolved
The problem of.In order to reduce influence of the random noise to measurement result, researcher, which is respectively adopted, improves system hardware and with number
The signal-to-noise ratio of word signal processing algorithm raising system.Wherein, although can be effectively inhibited using the method for improving system hardware
System random noise, but realize that difficulty is big and expensive, be not suitable for large-scale practical application.And data-signal is used to handle
Algorithm can be effectively reduced system random noise component in the case where not improving system hardware.But it is applied to divide at present
The noise reduction algorithm of cloth optical fiber temperature measurement system realizes that process is complicated, it is long to calculate the time, is not easy to practical application.
Summary of the invention
It solves the above problems the object of the invention is that providing one kind, the letter of acquired original signal can be effectively improved
Make an uproar ratio, reduce temperature measurement maximum deviation and root-mean-square error, improve the smoothness of temperature curve based on two points of SVD
Method for temperature-measuring system of distributed fibers noise reduction.
To achieve the goals above, the technical solution adopted by the present invention is that such: one kind is based on two points of SVD for being distributed
The method of formula optical fiber temperature measurement system noise reduction, comprising the following steps:
(1) the anti-Stokes signal and Stokes signal in optical fiber temperature measurement system are acquired, it is former to obtain anti-Stokes
Beginning data and Stokes initial data, are respectively configured to the Hankel matrix of 2 ╳ (L-1) dimension, and the L is initial data
Length；
(2) two Hankel matrixes are subjected to singular value decomposition respectively and reconstructed, the matrix after forming two noise reductions；
(21) Hankel matrix is carried out singular value decomposition is left singular matrix, singular value diagonal matrix and right unusual square
Battle array；
(22) first singular value of singular value diagonal matrix is retained, other singular values set 0, form new singular value pair
Angular moment battle array；
(23) it reconstructs left singular matrix, right singular matrix and new singular value diagonal matrix to form new matrix, mark
For the matrix after noise reduction；
(3) from the matrix after two noise reductions, anti-Stokes data and Stokes data is reacquired, are labeled as
Anti-Stokes noise reduction data and Stokes noise reduction data；
(4) according to anti-Stokes noise reduction data and Stokes noise reduction data, temperature measurement result is obtained；
(5) according to temperature measurement result, three indexs of temperature measurement result are calculated, three indexs are maximum inclined
Difference, root-mean-square error and smoothness；
(6) step (2)-(5) are repeated, until three indexs under current iteration number are all larger than under previous the number of iterations
Three indexs, the number of iterations at this time is maximum number of iterations n, stops repeating；
(7) three indexs for obtaining each iteration are ranked up, and when obtaining iteration j, the size of maximum deviation is in n
It is pressed in n times iteration from big in secondary iteration by the size of the precedence h (0 < h≤n) of descending order arrangement, root-mean-square error
The precedence w arranged in n times iteration by descending order to the size of small tactic precedence k (0 < k≤n), smoothness
(0 < w≤n), wherein j is positive integer, and 0 < j≤n
(8) the outcome quality coefficient Q of iteration j is calculatedj=h+k+w obtains maximum outcome quality Coefficient m ax { Qj}
=Qp, wherein 0 < j, p≤n, p is optimal the number of iterations；
(9) corresponding anti-Stokes noise reduction data and Stokes noise reduction data when pth time iteration are obtained, as noise reduction
Effect optimum signal.
As preferred: step (3) obtains the method for noise reduction data from the matrix after a noise reduction are as follows:
(31) using the data of the first row first row of the matrix after noise reduction and its second row L-1 column data as
First data and the last one data of acquisition data after the noise reduction；
(32) by the of the data of the 1st row i-th of the matrix after noise reduction column (i=2,3 ..., L-1) and the new matrix
The data addition of 2 rows i-th column is averaged, and using the average value as i-th of data of the acquisition data after the noise reduction.
As preferred: step (4) specifically:
(41) the anti-Stokes signal acquisition data under different temperatures after noise reduction and the Stokes after noise reduction are successively obtained
The ratio of signal acquisition data；
(42) according to the ratio, temperature experiment curv is obtained by curve matching；
(43) temperature measurement result is obtained according to the temperature experiment curv.
As preferred: three indexs in step (5) specifically:
Maximum deviation:
In formula,For the temperature measurement result that step (4) obtains, TactFor the reference temperature measured by mercurial thermometer；
N is the valid data number of measurement result；
Root-mean-square error is the root-mean-square error of temperature measurement result and reference temperature, and value is smaller, shows whole measurement
As a result it differs smaller with reference temperature, indicates are as follows:
Smoothness measures the degree of fluctuation of temperature measurement result, and value is smaller, shows that temperature experiment curv is more smooth, measures
As a result smaller, expression is fluctuated are as follows:
Compared with the prior art, the advantages of the present invention are as follows:
It first obtains two groups of initial data and is configured to two matrixes, two groups of initial data are anti-Stokes and Stokes former
Beginning data；Two matrixes are subjected to singular value decomposition respectively and are reconstructed, the matrix after forming two noise reductions；From the matrix after noise reduction
The middle data for obtaining noise reduction respectively, can be effectively reduced the noise level of data, convenient for demodulating temperature measurement result.
Go out temperature measurement result further according to two groups of noise reduction data demodulations, maximum deviation is calculated according to temperature measurement result,
Root-mean-square error and smoothness, and by above-mentioned three indexs come calculated result quality coefficient, convenient for according to outcome quality coefficient
It determines best iteration coefficient, to obtain the optimal signal of noise reduction effect, and then obtains the optimal temperature measurement knot of three indexs
Fruit.
Using two points of singular value decompositions of successive ignition, the result matter of de-noising signal demodulation result after each iteration is obtained
Coefficient of discharge；To find best the number of iterations, the corresponding noise reduction data of best the number of iterations are made into optimum signal.
Noise-reduction method provided by the invention effectively improves the signal-to-noise ratio of acquired original signal, and reduces temperature measurement
Maximum deviation and root-mean-square error, and improve the smoothness of temperature curve, and without changing system hardware structure, be convenient for
It realizes.
Detailed description of the invention
Fig. 1 is the schematic diagram of optical fiber temperature measurement system；
Fig. 2 is flow chart of the present invention；
Fig. 3 is the flow chart that Hankel matrix carries out singular value decomposition and reconstructs；
Fig. 4 is the comparison diagram of initial data and the optimum temperature measurement result obtained using the present invention at 50.0 DEG C；
The noise reduction effect comparison diagram of Fig. 5 a several noise-reduction methods when being 42.5 DEG C；
The noise reduction effect comparison diagram of Fig. 5 b several noise-reduction methods when being 50.0 DEG C；
The noise reduction effect comparison diagram of Fig. 5 c several noise-reduction methods when being 55.0 DEG C；
The noise reduction effect comparison diagram of Fig. 5 d several noise-reduction methods when being 60.0 DEG C；
Three indicatrixs of 5 iteration when Fig. 6 a is 45.2 DEG C；
Fig. 6 b is the outcome quality coefficient figure by Fig. 6 a 5 iteration calculated；
Three indicatrixs of 5 iteration when Fig. 7 a is 50.0 DEG C；
Fig. 7 b is the outcome quality coefficient figure by Fig. 7 a 5 iteration calculated；
Three indicatrixs of 5 iteration when Fig. 8 a is 55.0 DEG C；
Fig. 8 b is the outcome quality coefficient figure by Fig. 8 a 5 iteration calculated；
Three indicatrixs of 5 iteration when Fig. 9 a is 60.0 DEG C；
Fig. 9 b is the outcome quality coefficient figure by Fig. 9 a 5 iteration calculated；
Figure 10 is the maximum deviation comparison diagram that original signal is directly demodulated and demodulated after distinct methods are handled；
Figure 11 is the temperature root-mean-square error comparison diagram that original signal is directly demodulated and demodulated after distinct methods are handled；
Figure 12 is the temperature curve smoothness comparison diagram that original signal is directly demodulated and demodulated after distinct methods are handled；
Figure 13 is to remove the temperature curve smoothness figure comparison diagram after original signal in Figure 12.
Specific embodiment
The present invention will be further described with reference to the accompanying drawings.
Embodiment 1: referring to Fig. 1 and Fig. 2, optical fiber temperature measurement system includes: laser pulse light source, wavelength division multiplexer, sense light
Fibre, avalanche photodide, amplifier, data collecting card.Its working principle is that: laser pulse light source, which injects laser pulse, to be passed
Photosensitive fibre, laser pulse and the effect of sensor fibre medium generate the backward spontaneous Raman scattering light for having temperature information, with temperature
The backward spontaneous Raman scattering light of degree information is divided into anti-Stokes light and Stokes light, two ways of optical signals through wavelength division multiplexer
It is respectively converted into anti-Stokes electric signal and Stokes electric signal by avalanche photodide, two path signal is through amplifying
After device amplification, then through data collecting card obtain anti-Stokes data and Stokes data.
A method of temperature-measuring system of distributed fibers noise reduction is used for based on two points of SVD, comprising the following steps:
(1) the anti-Stokes signal and Stokes signal in optical fiber temperature measurement system are acquired, it is former to obtain anti-Stokes
Beginning data and Stokes initial data, are respectively configured to the Hankel matrix of 2 ╳ (L-1) dimension, and the L is initial data
Length；
(2) two Hankel matrixes are subjected to singular value decomposition respectively and reconstructed, the matrix after forming two noise reductions；
(21) Hankel matrix is carried out singular value decomposition is left singular matrix, singular value diagonal matrix and right unusual square
Battle array；
(22) first singular value of singular value diagonal matrix is retained, other singular values set 0, form new singular value pair
Angular moment battle array；
(23) it reconstructs left singular matrix, right singular matrix and new singular value diagonal matrix to form new matrix, mark
For the matrix after noise reduction；
(3) from the matrix after two noise reductions, anti-Stokes data and Stokes data is reacquired, are labeled as
Anti-Stokes noise reduction data and Stokes noise reduction data are obtained from the matrix after a noise reduction for a matrix
The method of noise reduction data are as follows:
(31) using the data of the first row first row of the matrix after noise reduction and its second row L-1 column data as
First data and the last one data of acquisition data after the noise reduction；
(32) by the of the data of the 1st row i-th of the matrix after noise reduction column (i=2,3 ..., L-1) and the new matrix
The data addition of 2 rows i-th column is averaged, and using the average value as i-th of data of the acquisition data after the noise reduction.
(4) according to anti-Stokes noise reduction data and Stokes noise reduction data, temperature measurement result is obtained, it is specific to use
Following methods:
(41) the anti-Stokes signal acquisition data under different temperatures after noise reduction and the Stokes after noise reduction are successively obtained
The ratio of signal acquisition data；
(42) according to the ratio, temperature experiment curv is obtained by curve matching；
(43) temperature measurement result is obtained according to the temperature experiment curv.
(5) according to temperature measurement result, three indexs of temperature measurement result are calculated, three indexs are maximum inclined
Difference, root-mean-square error and smoothness, calculation method are as follows:
Maximum deviation:
In formula,For the temperature measurement result that step (4) obtains, TactFor the reference temperature measured by mercurial thermometer；
N is the valid data number of measurement result；
Root-mean-square error is the root-mean-square error of temperature measurement result and reference temperature, and value is smaller, shows whole measurement
As a result it differs smaller with reference temperature, indicates are as follows:
Smoothness measures the degree of fluctuation of temperature measurement result, and value is smaller, shows that temperature experiment curv is more smooth, measures
As a result smaller, expression is fluctuated are as follows:
(6) step (2)-(5) are repeated, until three indexs under current iteration number are all larger than under previous the number of iterations
Three indexs, the number of iterations at this time are maximum number of iterations n, stop repeating；
(7) three indexs for obtaining each iteration are ranked up, and when obtaining iteration j, the size of maximum deviation is in n
It is pressed in n times iteration from big in secondary iteration by the size of the precedence h (0 < h≤n) of descending order arrangement, root-mean-square error
The precedence w arranged in n times iteration by descending order to the size of small tactic precedence k (0 < k≤n), smoothness
(0 < w≤n), wherein j is positive integer, and 0 < j≤n；
(8) the outcome quality coefficient Q of iteration j is calculatedj=h+k+w obtains maximum outcome quality Coefficient m ax { Qj}
=Qp, wherein 0 < j, p≤n, p is optimal the number of iterations；
(9) corresponding anti-Stokes noise reduction data and Stokes noise reduction data when pth time iteration are obtained, as noise reduction
Effect optimum signal.
Embodiment 2: referring to Fig. 1 to Figure 13, it is found that the data that we get include anti-Stokes original number in figure
According to Stokes initial data, in step (1)-(3), the processing of anti-Stokes initial data and Stokes initial data
Method is just the same.We are by taking actual temperature is the anti-Stokes initial data obtained at 45.2 DEG C as an example:
Anti-Stokes initial data is configured to the Hankel matrix of 2 ╳ (L-1) dimension, the L is initial data
Length；
It is left singular matrix, singular value diagonal matrix and right singular matrix that Hankel matrix, which is carried out singular value decomposition, again；
First singular value of singular value diagonal matrix is retained, other singular values set 0, form new singular value diagonal matrix；It will be left
Singular matrix, right singular matrix and new singular value diagonal matrix reconstruct to form new matrix, labeled as the matrix after noise reduction；Step
Suddenly matrix reconstruction is carried out in (2), is exactly the process of two points of SVD noise reductions；
Again from the matrix after noise reduction, anti-Stokes noise reduction data, Stokes original data processing method are reacquired
Ibid, one group of Stokes noise reduction data is finally obtained；
Go out temperature measurement result further according to anti-Stokes noise reduction data and Stokes noise reduction data demodulation, further according to temperature
Degree measurement result calculates three indexs of the temperature measurement result under the secondary iteration, and then (7) obtain most according to claim 1
Big the number of iterations, then the outcome quality coefficient under each the number of iterations is calculated, best change is obtained then according to outcome quality coefficient
Then generation number obtains the optimal data of noise reduction effect according to best the number of iterations, most preferably according to the optimal data of noise reduction effect
Obtain three optimal temperature measurement results of index.
Similarly: we 50.0 DEG C, 55.0 DEG C and 60.0 DEG C at a temperature of repeat above-mentioned work.Obtain four groups of different surveys
Measure result.
It referring to fig. 4, is the comparison diagram of initial data and the optimum temperature measurement result obtained using the present invention at 50.0 DEG C；
It can be seen from the figure that temperature jump part, the temperature demodulation result after two points of SVD noise reductions of the invention is compared to original number
It is gentler according to temperature demodulation result.
Referring to Fig. 5, at a temperature of Fig. 5 a, 5b, 5c, 5d are four kinds, the noise reduction effect comparison diagram of several noise-reduction methods；Every width figure
In be four lines, respectively initial data, small echo hard -threshold noise reduction, wavelet soft-threshold noise reduction and two points of SVD of the invention drop
The comparison made an uproar.
From Figure 10-Figure 13 it is found that three indexs of two points of SVD method processing results are superior to the processing knot of wavelet thresholding method
Fruit.
All in all, compared with initial data temperature demodulation acquired results, at 45.2 DEG C, 50.0 DEG C, 55.0 DEG C and 60.0
At DEG C, 1.59 DEG C, 2.49 DEG C, 1.22 DEG C and 0.87 DEG C are reduced respectively with the maximum deviation of this method temperature measurement result,
Square error reduces by 0.79,0.95,0.67 and 0.40 respectively, and the smoothness of curve is obviously improved, also, maximum deviation,
The result obtained by wavelet thresholding methods noise reduction is superior in three indexs of root-mean-square error and the smoothness of curve.
Claims (4)
1. a kind of method for being used for temperature-measuring system of distributed fibers noise reduction based on two points of SVD, it is characterised in that: including following step
It is rapid:
(1) the anti-Stokes signal and Stokes signal in optical fiber temperature measurement system are acquired, anti-Stokes original number is obtained
According to Stokes initial data, be respectively configured to 2 ╳ (L-1) dimension Hankel matrix, the L be initial data length
Degree；
(2) two Hankel matrixes are subjected to singular value decomposition respectively and reconstructed, the matrix after forming two noise reductions；
(21) Hankel matrix is carried out singular value decomposition is left singular matrix, singular value diagonal matrix and right singular matrix；
(22) first singular value of singular value diagonal matrix is retained, other singular values set 0, form new singular value to angular moment
Battle array；
(23) left singular matrix, right singular matrix and new singular value diagonal matrix are reconstructed and forms new matrix, labeled as drop
Matrix after making an uproar；
(3) from the matrix after two noise reductions, anti-Stokes data and Stokes data are reacquired, are labeled as anti-
Stokes noise reduction data and Stokes noise reduction data；
(4) according to anti-Stokes noise reduction data and Stokes noise reduction data, temperature measurement result is obtained；
(5) according to temperature measurement result, three indexs of temperature measurement result are calculated, three indexs are maximum deviation,
Square error and smoothness；
(6) step (2)-(5) are repeated, until three indexs under current iteration number are all larger than three under previous the number of iterations
Item index, the number of iterations at this time are maximum number of iterations n, stop repeating；
(7) three indexs for obtaining each iteration are ranked up, and when obtaining iteration j, the size of maximum deviation changes in n times
It is pressed from big to small in n times iteration in generation by the size of the precedence h (0 < h≤n) of descending order arrangement, root-mean-square error
Tactic precedence k (0 < k≤n), smoothness size in n times iteration by descending order arrangement precedence w (0 <
W≤n), wherein j is positive integer, and 0 < j≤n；
(8) the outcome quality coefficient Q of iteration j is calculatedj=h+k+w obtains maximum outcome quality Coefficient m ax { Qj}=Qp,
Wherein 0 < j, p≤n, p is optimal the number of iterations；
(9) corresponding anti-Stokes noise reduction data and Stokes noise reduction data when pth time iteration are obtained, as noise reduction effect
Optimum signal.
2. the method according to claim 1 for being used for temperature-measuring system of distributed fibers noise reduction based on two points of SVD, feature exist
In: step (3) obtains the method for noise reduction data from the matrix after a noise reduction are as follows:
(31) using the data of the first row first row of the matrix after noise reduction and the data of its second row L-1 column as described
First data and the last one data of acquisition data after noise reduction；
(32) by the 2nd row of the data of the 1st row i-th of the matrix after noise reduction column (i=2,3 ..., L-1) and the new matrix
The data addition of i-th column is averaged, and using the average value as i-th of data of the acquisition data after the noise reduction.
3. the method according to claim 1 for being used for temperature-measuring system of distributed fibers noise reduction based on two points of SVD, feature exist
In: step (4) specifically:
(41) the anti-Stokes signal acquisition data under different temperatures after noise reduction and the Stokes signal after noise reduction are successively obtained
Acquire the ratio of data；
(42) according to the ratio, temperature experiment curv is obtained by curve matching；
(43) temperature measurement result is obtained according to the temperature experiment curv.
4. the method according to claim 1 for being used for temperature-measuring system of distributed fibers noise reduction based on two points of SVD, feature exist
In: three indexs in step (5) specifically:
Maximum deviation:
In formula,For the temperature measurement result that step (4) obtains, TactFor the reference temperature measured by mercurial thermometer；N is to survey
Measure the valid data number of result；
Root-mean-square error is the root-mean-square error of temperature measurement result and reference temperature, and value is smaller, shows whole measurement result
It differs smaller with reference temperature, indicates are as follows:
Smoothness measures the degree of fluctuation of temperature measurement result, and value is smaller, shows that temperature experiment curv is more smooth, measurement result
Fluctuate smaller, expression are as follows:
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